Update README.md

README.md

@@ -15,19 +15,6 @@ size_categories:
 <h1 align="center">Fin-Fact - Financial Fact-Checking Dataset</h1>
 
 
-## Table of Contents
-
-- [Overview](#overview)
-- [Dataset Description](#dataset-description)
-- [Dataset Usage](#dataset-usage)
-- [Leaderboard](#leaderboard)
-- [Dependencies](#dependencies)
-- [Run models for paper metrics](#run-models-for-paper-metrics)
-- [Citation](#citation)
-- [Contribution](#contribution)
-- [License](#license)
-- [Contact](#contact)
-
 ## Overview
 
 Welcome to the Fin-Fact repository! Fin-Fact is a comprehensive dataset designed specifically for financial fact-checking and explanation generation. This README provides an overview of the dataset, how to use it, and other relevant information. [Click here](https://arxiv.org/abs/2309.08793) to access the paper.
@@ -52,49 +39,6 @@ Fin-Fact is a valuable resource for researchers, data scientists, and fact-check
 
 4. **Fact Checking Experiments**: Train and evaluate machine learning models, including text and image analysis, using the dataset to enhance the accuracy of fact-checking systems.
 
-## Leaderboard
-
-## Dependencies
-We recommend you create an anaconda environment:
-
-`conda create --name finfact python=3.6 conda-build`
-
-Then, install Python requirements:
-
-`pip install -r requirements.txt`
-
-
-## Run models for paper metrics
-
-We provide scripts let you easily run our dataset on existing state-of-the-art models and re-create the metrics published in paper. You should be able to reproduce our results from the paper by following these instructions. Please post an issue if you're unable to do this.
-To run existing ANLI models for fact checking. 
-
-### Run:
-1. BART
-```bash
-python anli.py --model_name 'ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
-```
-2. RoBERTa
-```bash
-python anli.py --model_name 'ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
-```
-3. ELECTRA
-```bash
-python anli.py --model_name 'ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
-```
-4. AlBERT
-```bash
-python anli.py --model_name 'ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
-```
-5. XLNET
-```bash
-python anli.py --model_name 'ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
-```
-6. GPT-2
-```bash
-python gpt2_nli.py --model_name 'fractalego/fact-checking' --data_file finfact.json
-```
-
 
 ## Citation